A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data
| dc.contributor.author | Nguyen, Hoang Minh | |
| dc.contributor.author | Demir, Begüm | |
| dc.contributor.author | Dalponte, Michele | |
| dc.date.accessioned | 2020-01-16T15:03:28Z | |
| dc.date.available | 2020-01-16T15:03:28Z | |
| dc.date.issued | 2019-12-09 | |
| dc.date.updated | 2020-01-07T16:32:28Z | |
| dc.description.abstract | Tree species classification at individual tree crowns (ITCs) level, using remote-sensing data, requires the availability of a sufficient number of reliable reference samples (i.e., training samples) to be used in the learning phase of the classifier. The classification performance of the tree species is mainly affected by two main issues: (i) an imbalanced distribution of the tree species classes, and (ii) the presence of unreliable samples due to field collection errors, coordinate misalignments, and ITCs delineation errors. To address these problems, in this paper, we present a weighted Support Vector Machine (wSVM)-based approach for the detection of tree species at ITC level. The proposed approach initially extracts (i) different weights associated to different classes of tree species, to mitigate the effect of the imbalanced distribution of the classes; and (ii) different weights associated to different training samples according to their importance for the classification problem, to reduce the effect of unreliable samples. Then, in order to exploit different weights in the learning phase of the classifier a wSVM algorithm is used. The features to characterize the tree species at ITC level are extracted from both the elevation and intensity of airborne light detection and ranging (LiDAR) data. Experimental results obtained on two study areas located in the Italian Alps show the effectiveness of the proposed approach. | en |
| dc.description.sponsorship | EC/H2020/759764/EU/Accurate and Scalable Processing of Big Data in Earth Observation/BigEarth | en |
| dc.identifier.eissn | 2072-4292 | |
| dc.identifier.uri | https://depositonce.tu-berlin.de/handle/11303/10609 | |
| dc.identifier.uri | http://dx.doi.org/10.14279/depositonce-9535 | |
| dc.language.iso | en | en |
| dc.rights.uri | https://creativecommons.org/licenses/by/4.0/ | en |
| dc.subject.ddc | 620 Ingenieurwissenschaften und zugeordnete Tätigkeiten | de |
| dc.subject.other | LiDAR | en |
| dc.subject.other | tree species classification | en |
| dc.subject.other | support vector machines | en |
| dc.subject.other | weighed support vector machines | en |
| dc.title | A Weighted SVM-Based Approach to Tree Species Classification at Individual Tree Crown Level Using LiDAR Data | en |
| dc.type | Article | en |
| dc.type.version | publishedVersion | en |
| dcterms.bibliographicCitation.articlenumber | 2948 | en |
| dcterms.bibliographicCitation.doi | 10.3390/rs11242948 | en |
| dcterms.bibliographicCitation.issue | 24 | en |
| dcterms.bibliographicCitation.journaltitle | Remote Sensing | en |
| dcterms.bibliographicCitation.originalpublishername | MDPI | en |
| dcterms.bibliographicCitation.originalpublisherplace | Basel | en |
| dcterms.bibliographicCitation.volume | 11 | en |
| tub.accessrights.dnb | free | en |
| tub.affiliation | Fak. 4 Elektrotechnik und Informatik::Inst. Technische Informatik und Mikroelektronik::FG Remote Sensing Image Analysis Group | de |
| tub.affiliation.faculty | Fak. 4 Elektrotechnik und Informatik | de |
| tub.affiliation.group | FG Remote Sensing Image Analysis Group | de |
| tub.affiliation.institute | Inst. Technische Informatik und Mikroelektronik | de |
| tub.publisher.universityorinstitution | Technische Universität Berlin | en |